Overview

Dataset statistics

Number of variables18
Number of observations36474
Missing cells7370
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.2 MiB
Average record size in memory639.4 B

Variable types

Numeric7
Categorical10
Boolean1

Alerts

Time_Orderd has a high cardinality: 176 distinct values High cardinality
Time_Order_picked has a high cardinality: 193 distinct values High cardinality
Restaurant_latitude is highly correlated with Delivery_location_latitudeHigh correlation
Restaurant_longitude is highly correlated with Delivery_location_longitudeHigh correlation
Delivery_location_latitude is highly correlated with Restaurant_latitudeHigh correlation
Delivery_location_longitude is highly correlated with Restaurant_longitudeHigh correlation
Restaurant_latitude is highly correlated with Restaurant_longitude and 2 other fieldsHigh correlation
Restaurant_longitude is highly correlated with Restaurant_latitude and 2 other fieldsHigh correlation
Delivery_location_latitude is highly correlated with Restaurant_latitude and 2 other fieldsHigh correlation
Delivery_location_longitude is highly correlated with Restaurant_latitude and 2 other fieldsHigh correlation
Restaurant_latitude is highly correlated with Delivery_location_latitudeHigh correlation
Restaurant_longitude is highly correlated with Delivery_location_longitudeHigh correlation
Delivery_location_latitude is highly correlated with Restaurant_latitudeHigh correlation
Delivery_location_longitude is highly correlated with Restaurant_longitudeHigh correlation
Delivery_person_Age is highly correlated with Delivery_person_Ratings and 3 other fieldsHigh correlation
Delivery_person_Ratings is highly correlated with Delivery_person_Age and 3 other fieldsHigh correlation
Restaurant_latitude is highly correlated with Restaurant_longitude and 2 other fieldsHigh correlation
Restaurant_longitude is highly correlated with Delivery_person_Age and 4 other fieldsHigh correlation
Delivery_location_latitude is highly correlated with Restaurant_latitude and 2 other fieldsHigh correlation
Delivery_location_longitude is highly correlated with Restaurant_latitude and 2 other fieldsHigh correlation
Vehicle_condition is highly correlated with Delivery_person_Age and 2 other fieldsHigh correlation
Type_of_vehicle is highly correlated with Delivery_person_Age and 2 other fieldsHigh correlation
multiple_deliveries is highly correlated with Time_takenHigh correlation
Festival is highly correlated with Time_takenHigh correlation
Time_taken is highly correlated with multiple_deliveries and 1 other fieldsHigh correlation
Delivery_person_Age has 1504 (4.1%) missing values Missing
Delivery_person_Ratings has 1546 (4.2%) missing values Missing
Time_Orderd has 1409 (3.9%) missing values Missing
Weather has 492 (1.3%) missing values Missing
Road_traffic_density has 482 (1.3%) missing values Missing
multiple_deliveries has 792 (2.2%) missing values Missing
City has 958 (2.6%) missing values Missing
Restaurant_latitude has 2895 (7.9%) zeros Zeros
Restaurant_longitude has 2895 (7.9%) zeros Zeros

Reproduction

Analysis started2022-08-27 16:05:25.868769
Analysis finished2022-08-27 16:05:40.260381
Duration14.39 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Delivery_person_Age
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct22
Distinct (%)0.1%
Missing1504
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean29.57932514
Minimum15
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size285.1 KiB
2022-08-27T21:35:40.339384image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile20.45
Q125
median30
Q335
95-th percentile39
Maximum50
Range35
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.815759296
Coefficient of variation (CV)0.1966156858
Kurtosis-1.062652747
Mean29.57932514
Median Absolute Deviation (MAD)5
Skewness0.01275537863
Sum1034389
Variance33.82305619
MonotonicityNot monotonic
2022-08-27T21:35:40.435407image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
351825
 
5.0%
361813
 
5.0%
381801
 
4.9%
301779
 
4.9%
221774
 
4.9%
371767
 
4.8%
291765
 
4.8%
321762
 
4.8%
331750
 
4.8%
241749
 
4.8%
Other values (12)17185
47.1%
ValueCountFrequency (%)
1530
 
0.1%
201719
4.7%
211704
4.7%
221774
4.9%
231664
4.6%
241749
4.8%
251717
4.7%
261695
4.6%
271736
4.8%
281727
4.7%
ValueCountFrequency (%)
5041
 
0.1%
391712
4.7%
381801
4.9%
371767
4.8%
361813
5.0%
351825
5.0%
341723
4.7%
331750
4.8%
321762
4.8%
311717
4.7%

Delivery_person_Ratings
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct28
Distinct (%)0.1%
Missing1546
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean4.633574782
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size285.1 KiB
2022-08-27T21:35:40.543445image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q14.5
median4.7
Q34.9
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.333933149
Coefficient of variation (CV)0.07206814711
Kurtosis15.48293613
Mean4.633574782
Median Absolute Deviation (MAD)0.2
Skewness-2.470103294
Sum161841.5
Variance0.111511348
MonotonicityNot monotonic
2022-08-27T21:35:40.765155image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
4.85704
15.6%
4.75692
15.6%
4.95635
15.4%
4.65556
15.2%
53195
8.8%
4.52625
7.2%
4.11146
 
3.1%
4.31135
 
3.1%
4.41122
 
3.1%
4.21099
 
3.0%
Other values (18)2019
 
5.5%
(Missing)1546
 
4.2%
ValueCountFrequency (%)
130
0.1%
2.515
< 0.1%
2.614
< 0.1%
2.719
0.1%
2.814
< 0.1%
2.913
< 0.1%
35
 
< 0.1%
3.124
0.1%
3.222
0.1%
3.319
0.1%
ValueCountFrequency (%)
641
 
0.1%
53195
8.8%
4.95635
15.4%
4.85704
15.6%
4.75692
15.6%
4.65556
15.2%
4.52625
7.2%
4.41122
 
3.1%
4.31135
 
3.1%
4.21099
 
3.0%

Restaurant_latitude
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct635
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.0175992
Minimum-30.905562
Maximum30.914057
Zeros2895
Zeros (%)7.9%
Negative354
Negative (%)1.0%
Memory size285.1 KiB
2022-08-27T21:35:40.874236image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-30.905562
5-th percentile0
Q112.933284
median18.55144
Q322.728163
95-th percentile26.913987
Maximum30.914057
Range61.819619
Interquartile range (IQR)9.794879

Descriptive statistics

Standard deviation8.190672134
Coefficient of variation (CV)0.4813059725
Kurtosis3.813834451
Mean17.0175992
Median Absolute Deviation (MAD)5.484678
Skewness-1.383686322
Sum620699.9133
Variance67.08711
MonotonicityNot monotonic
2022-08-27T21:35:40.984255image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02895
 
7.9%
26.911378151
 
0.4%
26.88842146
 
0.4%
26.914142144
 
0.4%
19.131141144
 
0.4%
21.173493143
 
0.4%
23.371292143
 
0.4%
12.979166142
 
0.4%
17.41233141
 
0.4%
26.905287140
 
0.4%
Other values (625)32285
88.5%
ValueCountFrequency (%)
-30.9055621
 
< 0.1%
-30.9028722
< 0.1%
-30.8995841
 
< 0.1%
-30.8958173
< 0.1%
-30.8933841
 
< 0.1%
-30.8932441
 
< 0.1%
-30.8901841
 
< 0.1%
-30.8859151
 
< 0.1%
-30.8858141
 
< 0.1%
-30.8739882
< 0.1%
ValueCountFrequency (%)
30.91405735
0.1%
30.90556228
0.1%
30.90287223
0.1%
30.89999226
0.1%
30.89958430
0.1%
30.89581728
0.1%
30.89520433
0.1%
30.89338432
0.1%
30.89324426
0.1%
30.89323426
0.1%

Restaurant_longitude
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct503
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.23567533
Minimum-88.366217
Maximum88.433452
Zeros2895
Zeros (%)7.9%
Negative138
Negative (%)0.4%
Memory size285.1 KiB
2022-08-27T21:35:41.108289image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-88.366217
5-th percentile0
Q173.17
median75.898497
Q378.045359
95-th percentile85.325347
Maximum88.433452
Range176.799669
Interquartile range (IQR)4.875359

Descriptive statistics

Standard deviation22.94683429
Coefficient of variation (CV)0.3267119478
Kurtosis10.51608333
Mean70.23567533
Median Absolute Deviation (MAD)2.164046
Skewness-3.242196708
Sum2561776.022
Variance526.5572042
MonotonicityNot monotonic
2022-08-27T21:35:41.216300image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02895
 
7.9%
75.789034151
 
0.4%
75.800689146
 
0.4%
75.805704145
 
0.4%
85.327872144
 
0.4%
72.813074144
 
0.4%
72.801953143
 
0.4%
77.640709142
 
0.4%
75.797282141
 
0.4%
78.449654141
 
0.4%
Other values (493)32282
88.5%
ValueCountFrequency (%)
-88.3662171
< 0.1%
-88.3528851
< 0.1%
-88.3498431
< 0.1%
-88.3223371
< 0.1%
-85.339821
< 0.1%
-85.3354861
< 0.1%
-85.3257312
< 0.1%
-85.3254472
< 0.1%
-85.3251461
< 0.1%
-81.8601871
< 0.1%
ValueCountFrequency (%)
88.43345228
0.1%
88.43318732
0.1%
88.40058126
0.1%
88.40046727
0.1%
88.3933129
0.1%
88.39329433
0.1%
88.36862827
0.1%
88.3678322
0.1%
88.36621726
0.1%
88.36550734
0.1%

Delivery_location_latitude
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4347
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.47502819
Minimum0.01
Maximum31.054057
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size285.1 KiB
2022-08-27T21:35:41.342336image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.08
Q112.989096
median18.639156
Q322.785207
95-th percentile27.022313
Maximum31.054057
Range31.044057
Interquartile range (IQR)9.796111

Descriptive statistics

Standard deviation7.317831025
Coefficient of variation (CV)0.4187593259
Kurtosis0.2769021953
Mean17.47502819
Median Absolute Deviation (MAD)5.479376
Skewness-0.707343297
Sum637384.1782
Variance53.55065092
MonotonicityNot monotonic
2022-08-27T21:35:41.457368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.13277
 
0.8%
0.11273
 
0.7%
0.06270
 
0.7%
0.09268
 
0.7%
0.02264
 
0.7%
0.01262
 
0.7%
0.03261
 
0.7%
0.05258
 
0.7%
0.08257
 
0.7%
0.04255
 
0.7%
Other values (4337)33829
92.7%
ValueCountFrequency (%)
0.01262
0.7%
0.02264
0.7%
0.03261
0.7%
0.04255
0.7%
0.05258
0.7%
0.06270
0.7%
0.07250
0.7%
0.08257
0.7%
0.09268
0.7%
0.11273
0.7%
ValueCountFrequency (%)
31.0540571
 
< 0.1%
31.0455624
< 0.1%
31.0440574
< 0.1%
31.0428722
< 0.1%
31.0399923
< 0.1%
31.0395843
< 0.1%
31.0358174
< 0.1%
31.0355623
< 0.1%
31.0352044
< 0.1%
31.0333843
< 0.1%

Delivery_location_longitude
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4347
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.88584124
Minimum0.01
Maximum88.563452
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size285.1 KiB
2022-08-27T21:35:41.589079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.08
Q173.279083
median76.0034
Q378.112543
95-th percentile85.376842
Maximum88.563452
Range88.553452
Interquartile range (IQR)4.83346

Descriptive statistics

Standard deviation21.06738054
Coefficient of variation (CV)0.297201531
Kurtosis7.167204335
Mean70.88584124
Median Absolute Deviation (MAD)2.202319
Skewness-2.965856959
Sum2585490.173
Variance443.8345229
MonotonicityNot monotonic
2022-08-27T21:35:41.694110image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.13277
 
0.8%
0.11273
 
0.7%
0.06270
 
0.7%
0.09268
 
0.7%
0.02264
 
0.7%
0.01262
 
0.7%
0.03261
 
0.7%
0.05258
 
0.7%
0.08257
 
0.7%
0.04255
 
0.7%
Other values (4337)33829
92.7%
ValueCountFrequency (%)
0.01262
0.7%
0.02264
0.7%
0.03261
0.7%
0.04255
0.7%
0.05258
0.7%
0.06270
0.7%
0.07250
0.7%
0.08257
0.7%
0.09268
0.7%
0.11273
0.7%
ValueCountFrequency (%)
88.5634521
 
< 0.1%
88.5631873
< 0.1%
88.5434523
< 0.1%
88.5431873
< 0.1%
88.5305814
< 0.1%
88.5304672
< 0.1%
88.5234523
< 0.1%
88.523312
< 0.1%
88.5232942
< 0.1%
88.5231872
< 0.1%

Order_Date
Categorical

Distinct44
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
15-03-2022
 
975
05-03-2022
 
947
03-04-2022
 
943
13-03-2022
 
939
26-03-2022
 
939
Other values (39)
31731 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters364740
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15-03-2022
2nd row16-03-2022
3rd row31-03-2022
4th row20-03-2022
5th row30-03-2022

Common Values

ValueCountFrequency (%)
15-03-2022975
 
2.7%
05-03-2022947
 
2.6%
03-04-2022943
 
2.6%
13-03-2022939
 
2.6%
26-03-2022939
 
2.6%
03-03-2022931
 
2.6%
09-03-2022931
 
2.6%
05-04-2022930
 
2.5%
21-03-2022922
 
2.5%
07-03-2022918
 
2.5%
Other values (34)27099
74.3%

Length

2022-08-27T21:35:41.805130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
15-03-2022975
 
2.7%
05-03-2022947
 
2.6%
03-04-2022943
 
2.6%
13-03-2022939
 
2.6%
26-03-2022939
 
2.6%
03-03-2022931
 
2.6%
09-03-2022931
 
2.6%
05-04-2022930
 
2.5%
21-03-2022922
 
2.5%
07-03-2022918
 
2.5%
Other values (34)27099
74.3%

Most occurring characters

ValueCountFrequency (%)
2125808
34.5%
088338
24.2%
-72948
20.0%
331661
 
8.7%
119496
 
5.3%
48975
 
2.5%
54404
 
1.2%
63976
 
1.1%
73366
 
0.9%
83142
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number291792
80.0%
Dash Punctuation72948
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2125808
43.1%
088338
30.3%
331661
 
10.9%
119496
 
6.7%
48975
 
3.1%
54404
 
1.5%
63976
 
1.4%
73366
 
1.2%
83142
 
1.1%
92626
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
-72948
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common364740
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2125808
34.5%
088338
24.2%
-72948
20.0%
331661
 
8.7%
119496
 
5.3%
48975
 
2.5%
54404
 
1.2%
63976
 
1.1%
73366
 
0.9%
83142
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII364740
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2125808
34.5%
088338
24.2%
-72948
20.0%
331661
 
8.7%
119496
 
5.3%
48975
 
2.5%
54404
 
1.2%
63976
 
1.1%
73366
 
0.9%
83142
 
0.9%

Time_Orderd
Categorical

HIGH CARDINALITY
MISSING

Distinct176
Distinct (%)0.5%
Missing1409
Missing (%)3.9%
Memory size2.1 MiB
21:35
 
373
17:55
 
369
21:55
 
366
22:20
 
362
21:15
 
360
Other values (171)
33235 

Length

Max length5
Median length5
Mean length4.909739056
Min length4

Characters and Unicode

Total characters172160
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11:50
2nd row19:40
3rd row23:20
4th row20:60
5th row21:50

Common Values

ValueCountFrequency (%)
21:35373
 
1.0%
17:55369
 
1.0%
21:55366
 
1.0%
22:20362
 
1.0%
21:15360
 
1.0%
19:55355
 
1.0%
17:35351
 
1.0%
17:40351
 
1.0%
19:60351
 
1.0%
18:35350
 
1.0%
Other values (166)31477
86.3%
(Missing)1409
 
3.9%

Length

2022-08-27T21:35:41.911169image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
21:35373
 
1.1%
17:55369
 
1.1%
21:55366
 
1.0%
22:20362
 
1.0%
21:15360
 
1.0%
19:55355
 
1.0%
17:35351
 
1.0%
17:40351
 
1.0%
19:60351
 
1.0%
18:35350
 
1.0%
Other values (166)31477
89.8%

Most occurring characters

ValueCountFrequency (%)
:35065
20.4%
128823
16.7%
225287
14.7%
024212
14.1%
523048
13.4%
310687
 
6.2%
47071
 
4.1%
95258
 
3.1%
85182
 
3.0%
63813
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number137095
79.6%
Other Punctuation35065
 
20.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
128823
21.0%
225287
18.4%
024212
17.7%
523048
16.8%
310687
 
7.8%
47071
 
5.2%
95258
 
3.8%
85182
 
3.8%
63813
 
2.8%
73714
 
2.7%
Other Punctuation
ValueCountFrequency (%)
:35065
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common172160
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
:35065
20.4%
128823
16.7%
225287
14.7%
024212
14.1%
523048
13.4%
310687
 
6.2%
47071
 
4.1%
95258
 
3.1%
85182
 
3.0%
63813
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII172160
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
:35065
20.4%
128823
16.7%
225287
14.7%
024212
14.1%
523048
13.4%
310687
 
6.2%
47071
 
4.1%
95258
 
3.1%
85182
 
3.0%
63813
 
2.2%

Time_Order_picked
Categorical

HIGH CARDINALITY

Distinct193
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
21:30
 
386
17:55
 
380
18:05
 
377
21:45
 
375
22:25
 
370
Other values (188)
34586 

Length

Max length5
Median length5
Mean length4.918380216
Min length4

Characters and Unicode

Total characters179393
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12:05
2nd row19:45
3rd row23:25
4th row21:15
5th row22:05

Common Values

ValueCountFrequency (%)
21:30386
 
1.1%
17:55380
 
1.0%
18:05377
 
1.0%
21:45375
 
1.0%
22:25370
 
1.0%
22:40369
 
1.0%
19:60368
 
1.0%
23:50363
 
1.0%
23:05363
 
1.0%
18:45362
 
1.0%
Other values (183)32761
89.8%

Length

2022-08-27T21:35:42.021179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
21:30386
 
1.1%
17:55380
 
1.0%
18:05377
 
1.0%
21:45375
 
1.0%
22:25370
 
1.0%
22:40369
 
1.0%
19:60368
 
1.0%
23:50363
 
1.0%
23:05363
 
1.0%
18:45362
 
1.0%
Other values (183)32761
89.8%

Most occurring characters

ValueCountFrequency (%)
:36474
20.3%
127496
15.3%
026465
14.8%
526143
14.6%
226088
14.5%
311026
 
6.1%
47985
 
4.5%
95361
 
3.0%
85181
 
2.9%
63930
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number142919
79.7%
Other Punctuation36474
 
20.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
127496
19.2%
026465
18.5%
526143
18.3%
226088
18.3%
311026
7.7%
47985
 
5.6%
95361
 
3.8%
85181
 
3.6%
63930
 
2.7%
73244
 
2.3%
Other Punctuation
ValueCountFrequency (%)
:36474
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common179393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
:36474
20.3%
127496
15.3%
026465
14.8%
526143
14.6%
226088
14.5%
311026
 
6.1%
47985
 
4.5%
95361
 
3.0%
85181
 
2.9%
63930
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII179393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
:36474
20.3%
127496
15.3%
026465
14.8%
526143
14.6%
226088
14.5%
311026
 
6.1%
47985
 
4.5%
95361
 
3.0%
85181
 
2.9%
63930
 
2.2%

Weather
Categorical

MISSING

Distinct6
Distinct (%)< 0.1%
Missing492
Missing (%)1.3%
Memory size2.2 MiB
Fog
6119 
Stormy
6070 
Cloudy
6013 
Windy
5999 
Sandstorms
5959 

Length

Max length10
Median length6
Mean length5.823745206
Min length3

Characters and Unicode

Total characters209550
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCloudy
2nd rowSandstorms
3rd rowSandstorms
4th rowStormy
5th rowSandstorms

Common Values

ValueCountFrequency (%)
Fog6119
16.8%
Stormy6070
16.6%
Cloudy6013
16.5%
Windy5999
16.4%
Sandstorms5959
16.3%
Sunny5822
16.0%
(Missing)492
 
1.3%

Length

2022-08-27T21:35:42.132204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-27T21:35:42.270236image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
fog6119
17.0%
stormy6070
16.9%
cloudy6013
16.7%
windy5999
16.7%
sandstorms5959
16.6%
sunny5822
16.2%

Most occurring characters

ValueCountFrequency (%)
o24161
11.5%
y23904
11.4%
n23602
11.3%
d17971
8.6%
S17851
8.5%
r12029
 
5.7%
t12029
 
5.7%
m12029
 
5.7%
s11918
 
5.7%
u11835
 
5.6%
Other values (7)42221
20.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter173568
82.8%
Uppercase Letter35982
 
17.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o24161
13.9%
y23904
13.8%
n23602
13.6%
d17971
10.4%
r12029
6.9%
t12029
6.9%
m12029
6.9%
s11918
6.9%
u11835
6.8%
g6119
 
3.5%
Other values (3)17971
10.4%
Uppercase Letter
ValueCountFrequency (%)
S17851
49.6%
F6119
 
17.0%
C6013
 
16.7%
W5999
 
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin209550
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o24161
11.5%
y23904
11.4%
n23602
11.3%
d17971
8.6%
S17851
8.5%
r12029
 
5.7%
t12029
 
5.7%
m12029
 
5.7%
s11918
 
5.7%
u11835
 
5.6%
Other values (7)42221
20.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII209550
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o24161
11.5%
y23904
11.4%
n23602
11.3%
d17971
8.6%
S17851
8.5%
r12029
 
5.7%
t12029
 
5.7%
m12029
 
5.7%
s11918
 
5.7%
u11835
 
5.6%
Other values (7)42221
20.1%

Road_traffic_density
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing482
Missing (%)1.3%
Memory size2.1 MiB
Low
12366 
Jam
11325 
Medium
8761 
High
3540 

Length

Max length6
Median length3
Mean length3.8286008
Min length3

Characters and Unicode

Total characters137799
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh
2nd rowJam
3rd rowLow
4th rowJam
5th rowJam

Common Values

ValueCountFrequency (%)
Low12366
33.9%
Jam11325
31.0%
Medium8761
24.0%
High3540
 
9.7%
(Missing)482
 
1.3%

Length

2022-08-27T21:35:42.400265image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-27T21:35:42.514290image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
low12366
34.4%
jam11325
31.5%
medium8761
24.3%
high3540
 
9.8%

Most occurring characters

ValueCountFrequency (%)
m20086
14.6%
w12366
9.0%
o12366
9.0%
L12366
9.0%
i12301
8.9%
a11325
8.2%
J11325
8.2%
u8761
6.4%
d8761
6.4%
e8761
6.4%
Other values (4)19381
14.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter101807
73.9%
Uppercase Letter35992
 
26.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m20086
19.7%
w12366
12.1%
o12366
12.1%
i12301
12.1%
a11325
11.1%
u8761
8.6%
d8761
8.6%
e8761
8.6%
h3540
 
3.5%
g3540
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
L12366
34.4%
J11325
31.5%
M8761
24.3%
H3540
 
9.8%

Most occurring scripts

ValueCountFrequency (%)
Latin137799
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m20086
14.6%
w12366
9.0%
o12366
9.0%
L12366
9.0%
i12301
8.9%
a11325
8.2%
J11325
8.2%
u8761
6.4%
d8761
6.4%
e8761
6.4%
Other values (4)19381
14.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII137799
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m20086
14.6%
w12366
9.0%
o12366
9.0%
L12366
9.0%
i12301
8.9%
a11325
8.2%
J11325
8.2%
u8761
6.4%
d8761
6.4%
e8761
6.4%
Other values (4)19381
14.1%

Vehicle_condition
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
2
12079 
0
12016 
1
11964 
3
 
415

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36474
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
212079
33.1%
012016
32.9%
111964
32.8%
3415
 
1.1%

Length

2022-08-27T21:35:42.628317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-27T21:35:42.743343image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
212079
33.1%
012016
32.9%
111964
32.8%
3415
 
1.1%

Most occurring characters

ValueCountFrequency (%)
212079
33.1%
012016
32.9%
111964
32.8%
3415
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36474
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
212079
33.1%
012016
32.9%
111964
32.8%
3415
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common36474
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
212079
33.1%
012016
32.9%
111964
32.8%
3415
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII36474
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
212079
33.1%
012016
32.9%
111964
32.8%
3415
 
1.1%

Type_of_order
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
Snack
9232 
Meal
9202 
Buffet
9030 
Drinks
9010 

Length

Max length6
Median length5
Mean length5.24230959
Min length4

Characters and Unicode

Total characters191208
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBuffet
2nd rowBuffet
3rd rowMeal
4th rowSnack
5th rowDrinks

Common Values

ValueCountFrequency (%)
Snack9232
25.3%
Meal9202
25.2%
Buffet9030
24.8%
Drinks9010
24.7%

Length

2022-08-27T21:35:42.865370image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-27T21:35:42.994399image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
snack9232
25.3%
meal9202
25.2%
buffet9030
24.8%
drinks9010
24.7%

Most occurring characters

ValueCountFrequency (%)
a18434
 
9.6%
k18242
 
9.5%
n18242
 
9.5%
e18232
 
9.5%
f18060
 
9.4%
c9232
 
4.8%
S9232
 
4.8%
l9202
 
4.8%
M9202
 
4.8%
t9030
 
4.7%
Other values (6)54100
28.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter154734
80.9%
Uppercase Letter36474
 
19.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a18434
11.9%
k18242
11.8%
n18242
11.8%
e18232
11.8%
f18060
11.7%
c9232
6.0%
l9202
5.9%
t9030
5.8%
u9030
5.8%
s9010
5.8%
Other values (2)18020
11.6%
Uppercase Letter
ValueCountFrequency (%)
S9232
25.3%
M9202
25.2%
B9030
24.8%
D9010
24.7%

Most occurring scripts

ValueCountFrequency (%)
Latin191208
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a18434
 
9.6%
k18242
 
9.5%
n18242
 
9.5%
e18232
 
9.5%
f18060
 
9.4%
c9232
 
4.8%
S9232
 
4.8%
l9202
 
4.8%
M9202
 
4.8%
t9030
 
4.7%
Other values (6)54100
28.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII191208
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a18434
 
9.6%
k18242
 
9.5%
n18242
 
9.5%
e18232
 
9.5%
f18060
 
9.4%
c9232
 
4.8%
S9232
 
4.8%
l9202
 
4.8%
M9202
 
4.8%
t9030
 
4.7%
Other values (6)54100
28.3%

Type_of_vehicle
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
motorcycle
21144 
scooter
12189 
electric_scooter
3085 
bicycle
 
56

Length

Max length16
Median length10
Mean length9.500329001
Min length7

Characters and Unicode

Total characters346515
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowscooter
2nd rowmotorcycle
3rd rowmotorcycle
4th rowmotorcycle
5th rowelectric_scooter

Common Values

ValueCountFrequency (%)
motorcycle21144
58.0%
scooter12189
33.4%
electric_scooter3085
 
8.5%
bicycle56
 
0.2%

Length

2022-08-27T21:35:43.110425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-27T21:35:43.228452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
motorcycle21144
58.0%
scooter12189
33.4%
electric_scooter3085
 
8.5%
bicycle56
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o72836
21.0%
c63844
18.4%
e42644
12.3%
r39503
11.4%
t39503
11.4%
l24285
 
7.0%
y21200
 
6.1%
m21144
 
6.1%
s15274
 
4.4%
i3141
 
0.9%
Other values (2)3141
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter343430
99.1%
Connector Punctuation3085
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o72836
21.2%
c63844
18.6%
e42644
12.4%
r39503
11.5%
t39503
11.5%
l24285
 
7.1%
y21200
 
6.2%
m21144
 
6.2%
s15274
 
4.4%
i3141
 
0.9%
Connector Punctuation
ValueCountFrequency (%)
_3085
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin343430
99.1%
Common3085
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o72836
21.2%
c63844
18.6%
e42644
12.4%
r39503
11.5%
t39503
11.5%
l24285
 
7.1%
y21200
 
6.2%
m21144
 
6.2%
s15274
 
4.4%
i3141
 
0.9%
Common
ValueCountFrequency (%)
_3085
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII346515
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o72836
21.0%
c63844
18.4%
e42644
12.3%
r39503
11.4%
t39503
11.4%
l24285
 
7.0%
y21200
 
6.1%
m21144
 
6.1%
s15274
 
4.4%
i3141
 
0.9%
Other values (2)3141
 
0.9%

multiple_deliveries
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing792
Missing (%)2.2%
Memory size2.1 MiB
1.0
22569 
0.0
11268 
2.0
 
1552
3.0
 
293

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters107046
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.022569
61.9%
0.011268
30.9%
2.01552
 
4.3%
3.0293
 
0.8%
(Missing)792
 
2.2%

Length

2022-08-27T21:35:43.344160image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-27T21:35:43.458187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.022569
63.3%
0.011268
31.6%
2.01552
 
4.3%
3.0293
 
0.8%

Most occurring characters

ValueCountFrequency (%)
046950
43.9%
.35682
33.3%
122569
21.1%
21552
 
1.4%
3293
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number71364
66.7%
Other Punctuation35682
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
046950
65.8%
122569
31.6%
21552
 
2.2%
3293
 
0.4%
Other Punctuation
ValueCountFrequency (%)
.35682
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common107046
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
046950
43.9%
.35682
33.3%
122569
21.1%
21552
 
1.4%
3293
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII107046
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
046950
43.9%
.35682
33.3%
122569
21.1%
21552
 
1.4%
3293
 
0.3%

Festival
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing187
Missing (%)0.5%
Memory size71.4 KiB
False
35574 
True
 
713
(Missing)
 
187
ValueCountFrequency (%)
False35574
97.5%
True713
 
2.0%
(Missing)187
 
0.5%
2022-08-27T21:35:43.572212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

City
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing958
Missing (%)2.6%
Memory size2.3 MiB
Metropolitian
27315 
Urban
8074 
Semi-Urban
 
127

Length

Max length13
Median length13
Mean length11.17059917
Min length5

Characters and Unicode

Total characters396735
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMetropolitian
2nd rowMetropolitian
3rd rowMetropolitian
4th rowMetropolitian
5th rowMetropolitian

Common Values

ValueCountFrequency (%)
Metropolitian27315
74.9%
Urban8074
 
22.1%
Semi-Urban127
 
0.3%
(Missing)958
 
2.6%

Length

2022-08-27T21:35:43.674234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-27T21:35:43.792261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
metropolitian27315
76.9%
urban8074
 
22.7%
semi-urban127
 
0.4%

Most occurring characters

ValueCountFrequency (%)
i54757
13.8%
o54630
13.8%
t54630
13.8%
n35516
9.0%
a35516
9.0%
r35516
9.0%
e27442
6.9%
l27315
6.9%
p27315
6.9%
M27315
6.9%
Other values (5)16783
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter360965
91.0%
Uppercase Letter35643
 
9.0%
Dash Punctuation127
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i54757
15.2%
o54630
15.1%
t54630
15.1%
n35516
9.8%
a35516
9.8%
r35516
9.8%
e27442
7.6%
l27315
7.6%
p27315
7.6%
b8201
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
M27315
76.6%
U8201
 
23.0%
S127
 
0.4%
Dash Punctuation
ValueCountFrequency (%)
-127
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin396608
> 99.9%
Common127
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i54757
13.8%
o54630
13.8%
t54630
13.8%
n35516
9.0%
a35516
9.0%
r35516
9.0%
e27442
6.9%
l27315
6.9%
p27315
6.9%
M27315
6.9%
Other values (4)16656
 
4.2%
Common
ValueCountFrequency (%)
-127
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII396735
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i54757
13.8%
o54630
13.8%
t54630
13.8%
n35516
9.0%
a35516
9.0%
r35516
9.0%
e27442
6.9%
l27315
6.9%
p27315
6.9%
M27315
6.9%
Other values (5)16783
 
4.2%

Time_taken
Real number (ℝ≥0)

HIGH CORRELATION

Distinct45
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.28639579
Minimum10
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size285.1 KiB
2022-08-27T21:35:43.903279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile13
Q119
median26
Q332
95-th percentile44
Maximum54
Range44
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.38207989
Coefficient of variation (CV)0.3569176986
Kurtosis-0.3118708603
Mean26.28639579
Median Absolute Deviation (MAD)7
Skewness0.4845153376
Sum958770
Variance88.02342307
MonotonicityNot monotonic
2022-08-27T21:35:44.012298image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
261669
 
4.6%
251643
 
4.5%
271618
 
4.4%
281577
 
4.3%
291557
 
4.3%
151475
 
4.0%
191448
 
4.0%
181413
 
3.9%
161378
 
3.8%
171354
 
3.7%
Other values (35)21342
58.5%
ValueCountFrequency (%)
10600
1.6%
11610
1.7%
12597
1.6%
13570
 
1.6%
14586
 
1.6%
151475
4.0%
161378
3.8%
171354
3.7%
181413
3.9%
191448
4.0%
ValueCountFrequency (%)
5470
 
0.2%
5379
 
0.2%
5267
 
0.2%
5175
 
0.2%
5054
 
0.1%
49228
0.6%
48230
0.6%
47228
0.6%
46203
0.6%
45198
0.5%

Interactions

2022-08-27T21:35:38.101018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:32.468198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:33.440437image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:34.335639image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:35.269874image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:36.291104image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:37.218314image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:38.234047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:32.631249image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:33.575481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:34.476693image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:35.409905image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:36.430136image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:37.351344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:38.358176image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:32.767285image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:33.702499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:34.609739image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:35.539934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:36.562167image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:37.475386image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:38.485204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:32.905324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:33.831544image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:34.746755image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:35.674964image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:36.697195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:37.604400image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:38.614235image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:33.044361image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:33.960570image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:34.882785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:35.905017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:36.832225image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:37.733429image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:38.742263image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:33.182392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:34.091591image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:35.019818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:36.040047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:36.968257image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:37.863459image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:38.860668image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:33.311422image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:34.214630image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:35.144844image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:36.165075image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:37.093305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-08-27T21:35:37.981500image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2022-08-27T21:35:44.119340image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-27T21:35:44.474421image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-27T21:35:44.670464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-27T21:35:44.865574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-08-27T21:35:45.044614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-27T21:35:39.098721image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-27T21:35:39.487162image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-08-27T21:35:39.850291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-08-27T21:35:40.076340image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Delivery_person_AgeDelivery_person_RatingsRestaurant_latitudeRestaurant_longitudeDelivery_location_latitudeDelivery_location_longitudeOrder_DateTime_OrderdTime_Order_pickedWeatherRoad_traffic_densityVehicle_conditionType_of_orderType_of_vehiclemultiple_deliveriesFestivalCityTime_taken
032.04.612.31097276.65926412.33097276.67926415-03-202211:5012:05CloudyHigh1Buffetscooter1.0NoMetropolitian32.0
132.04.522.76122675.88752222.85122675.97752216-03-202219:4019:45SandstormsJam1Buffetmotorcycle0.0NoMetropolitian37.0
238.04.921.17510472.80434221.25510472.88434231-03-202223:2023:25SandstormsLow2Mealmotorcycle1.0NoMetropolitian18.0
327.04.726.91192775.79728226.99192775.87728220-03-202220:6021:15StormyJam1Snackmotorcycle1.0NoMetropolitian29.0
428.04.812.29952476.64262012.34952476.69262030-03-202221:5022:05SandstormsJam2Drinkselectric_scooter1.0NoMetropolitian14.0
520.04.523.35919485.32544723.40919485.37544711-03-202221:4521:55WindyJam2Mealscooter0.0NoMetropolitian19.0
623.05.013.00580180.25074413.09580180.34074412-03-202217:4017:45SandstormsMedium1Drinksscooter0.0NoMetropolitian28.0
721.04.623.35442285.33290023.43442285.41290004-03-202217:5017:60WindyMedium1BuffetscooterNaNNoMetropolitian24.0
838.04.722.30809673.16775322.34809673.20775319-03-202214:5014:60SunnyHigh1Buffetscooter1.0NoMetropolitian23.0
934.03.513.02978080.20881213.09978080.27881220-03-202223:1523:20FogLow2Mealscooter0.0NoMetropolitian39.0

Last rows

Delivery_person_AgeDelivery_person_RatingsRestaurant_latitudeRestaurant_longitudeDelivery_location_latitudeDelivery_location_longitudeOrder_DateTime_OrderdTime_Order_pickedWeatherRoad_traffic_densityVehicle_conditionType_of_orderType_of_vehiclemultiple_deliveriesFestivalCityTime_taken
3646425.04.825.45031781.83168125.49031781.87168113-02-202215:4515:55StormyMedium2Mealmotorcycle0.0NoUrban22.0
3646533.04.419.87802875.31747520.00802875.44747512-02-202220:6021:05SandstormsJam0Buffetmotorcycle2.0NoMetropolitian48.0
3646626.0NaN0.0000000.0000000.0800000.08000016-02-202223:5023:55CloudyLow1Buffetscooter1.0NoMetropolitian28.0
3646726.04.912.97845377.64368513.03845377.70368505-03-202223:6024:05FogLow2Buffetelectric_scooter1.0NoUrban14.0
3646836.04.717.48321678.55211117.54321678.61211113-03-202217:2517:35FogMedium2Drinksscooter0.0NoUrban19.0
3646924.05.012.97216177.59601413.03216177.65601409-03-202223:3023:45SunnyLow2Drinkselectric_scooter0.0NoMetropolitian22.0
3647029.03.612.31097276.65926412.32097276.66926424-03-202211:3011:45SunnyHigh0Buffetmotorcycle3.0NoMetropolitian48.0
3647131.04.712.97022177.64539612.98022177.65539613-03-20228:158:25SunnyLow2Drinksscooter0.0NoNaN18.0
3647238.04.221.17597572.79550321.30597572.92550308-03-202218:1518:30CloudyMedium2Mealscooter1.0NoMetropolitian31.0
3647325.04.913.02701880.25479113.13701880.36479125-03-202220:3520:45StormyJam0Mealmotorcycle1.0NoMetropolitian34.0